Real-Time Prediction of Elbow Motion Through sEMG-Based Hybrid BP-LSTM Network

In the face of the large number of people with motor function disabilities, rehabilitation robots have attracted more and more attention. In order to promote the active participation of the user’s motion intention in the assisted rehabilitation process of the robots, it is crucial to establish the human motion prediction model. In this paper, a hybrid prediction model built on long short-term memory (LSTM) neural network using surface electromyography (sEMG) is applied to predict the elbow motion of the users in advance. This model includes two sub-models: a back-propagation neural network and an LSTM network. The former extracts a preliminary prediction of the elbow motion, and the latter corrects this prediction to increase accuracy. The proposed model takes time series data as input, which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units. The offline and online tests were carried out to verify the established hybrid model. Finally, average root mean square errors of 3.52° and 4.18° were reached respectively for offline and online tests, and the correlation coefficients for both were above 0.98.